1. Field of the Invention
This invention pertains generally to data processing for biological systems. More specifically, the present invention is a pharmacokinetics system for correlating in vitro and in vivo data and more particularly to a single tool to conduct these types of studies.
2. Description of the Related Art
Many prior art methods of obtaining biological process data require time consuming laboratory experiments. Data is usually obtained from live animal experiments and clinical trials which are costly and provide many difficult-to-control variables that may mask biochemical activities which are the response of interest. The complexity oft information does not always provide a clear and consistent picture from which accurate conclusions can be drawn.
In an effort to provide more clear and consistent test results, clinical trials are typically designed to isolate a single variable, and use a placebo control group as a baseline from which the variable is measured. Observations from a clinical trial are used to attempt to draw conclusions from apparent differences between the control group and the experimental group. These observations, however, rarely take into account the multi-variable dynamic nature of the patients, either individually or as a group. Such variations are, however, reflected in the data and require large test populations to deal with in an appropriate statistical manner.
A typical cycle for a clinical trial requires years of work. Designing the trial may take six months, performance of the trial may take a year, and analysis of the results may take yet another six months. After years of testing, the results may still be suspect. Additionally, a trial may be one of several ongoing trials necessary to address the variables associated with a particular area of investigation.
Only after numerous costly trial-and-error clinical trials, and constant redesigning of the clinical use of the drug to account for lessons learned from the most recent clinical trial, is a drug finally realized that has adequate safety and efficacy. This process of clinical trial design and redesign, multiple clinical trials and, in some situations, multiple drug redesigns, requires much time and money. Even then, the effort may not produce a marketable drug.
Owing to the cost and difficulty of the experiments, drugs that may be cost-effectively researched and developed using this type of modeling are few. They generally include either refinements to existing drugs, or an attempt to develop a drug for a new application that was inferred from observations made during previous clinical trials and experiments. The enormous risk prevents the development of pharmaceuticals for anything but an extremely large segment of the population. Biological abnormalities which may be treatable by a drug may not be explored, because the potential market for the drug does not justify the expenditure of resources necessary to design, test, and obtain approval for the drug. Even with large market segments, development is extremely speculative. In summary, the cost of drug development is very high and difficult to justify except for the largest of patient populations and lowest of risks.
Pharmacokinetic studies are used to assess the systemic exposure of administered drugs and factors likely to affect this exposure. The studies are desirably carried out in a well-controlled clinical environment. Samples are collected on each of the study subjects, and concentration-time data are analyzed to derive parameters such as the observed maximum concentration, Cmax, and the area under the concentration-time curve, AUC.
The statistical analysis of pharmacokinetic data addresses time-dependent repeated measurements of drug of concentrations in various organs of the body, with the goal to describe the time course and to determine clinically relevant parameters by modeling the organism through compartments and flow rates. The mathematical solution is a system of differential equations with an explicit solution for most of the one or two compartment models. Otherwise, numerical solutions have to be used. Intrinsic pharmacokinetic parameters include area under the curve (AUC), clearance, distribution volume, half time, elimination rates, minimum inhibitory concentrations, etc.
In addition to the prior art in vivo studies, a number of in vitro or cell culture-based methods have been described for identifying compounds with a particular biological effect. From these trials and experiments, data is obtained which usually focuses on a more specific part of the biological system, and avoids some oft variables that cannot otherwise be controlled. While conclusions may be drawn by assimilating experimental data and published information, it is difficult, if not impossible, for an individual or research team to synthesize the relationships among all the available data and knowledge. Consequently, it is highly desirable to provide advanced tools and techniques which enable the individual or research team to study whether there is a correlation between the data obtained from the testing methods. These correlations are important from the early development stages throughout the entire development and evaluation cycle. The data and the developed correlations are used to assist the scientist in understanding and optimizing pharmaceutical formulations. The FDA has recognized the utility of In Vitro In Vivo Correlation, hereinafter “IVIVC”, and has provided guidance pertaining thereto. The guidance provides recommendations to sponsors of new drug applications (NDA's), abbreviated new drug applications (ANDA's), and abbreviated antibiotic applications (AADA's). Specifically addressed are the Scale-Up and Post Approval Changes (herein after “SUPAC”), and notification requirements. IVIVC results can be utilized in the following development conditions: 1) As a surrogate to expensive bioequivalency studies, which may typically be required for SUPAC changes for instances involving minor manufacturing, formulation or strength changes; 2) To support and/or validate the use of dissolution testing and specifications as range setting parameters for quality control tool to measure process control; 3) To serve as a prediction toll to predict the in-vivo performance of a formulation using in-vitro dissolution data which can be applied to formulation design specifications in order to achieve optimal plasma concentration-time profiles; 4) To identify appropriate dissolution characteristics for a particular formulation which result in data relevant to in vivo performance. Within this guidance is the identification of situations where IVIVC data is acceptable in lieu of in vivo bioequivalence testing. What is needed then is an improved system and method which more efficiently reveals and conveys correlations between in vivo and in vitro results of tests performed on complex biological systems, which may be used by artisans in product development and in meeting governmental requirements.